{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(\"../../\")\n",
    "from Utils.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resumeId</th>\n",
       "      <th>username</th>\n",
       "      <th>gender</th>\n",
       "      <th>jobStatus</th>\n",
       "      <th>exp</th>\n",
       "      <th>expectPosition</th>\n",
       "      <th>willSalaryStart</th>\n",
       "      <th>willSalaryEnd</th>\n",
       "      <th>updateTime</th>\n",
       "      <th>province</th>\n",
       "      <th>...</th>\n",
       "      <th>学校1</th>\n",
       "      <th>专业1</th>\n",
       "      <th>学位1</th>\n",
       "      <th>起始时间1</th>\n",
       "      <th>毕业时间1</th>\n",
       "      <th>学校2</th>\n",
       "      <th>专业2</th>\n",
       "      <th>学位2</th>\n",
       "      <th>起始时间2</th>\n",
       "      <th>毕业时间2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1635610357617786880</td>\n",
       "      <td>欧先生</td>\n",
       "      <td>男</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师|数据挖掘工程师|算法工程师</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2023-03-14</td>\n",
       "      <td>广东省</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1634019092572798976</td>\n",
       "      <td>赖女士</td>\n",
       "      <td>女</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>4000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2023-03-10</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1630818546172952576</td>\n",
       "      <td>陈先生</td>\n",
       "      <td>男</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师|数据挖掘工程师|算法工程师</td>\n",
       "      <td>6000</td>\n",
       "      <td>8000</td>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>广东省</td>\n",
       "      <td>...</td>\n",
       "      <td>韩山师范学院</td>\n",
       "      <td>统计学</td>\n",
       "      <td>本科</td>\n",
       "      <td>2019-09-15</td>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1630884821167374336</td>\n",
       "      <td>李女士</td>\n",
       "      <td>女</td>\n",
       "      <td>无明确就业状态</td>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>5000</td>\n",
       "      <td>8000</td>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>广东省</td>\n",
       "      <td>...</td>\n",
       "      <td>韶关学院</td>\n",
       "      <td>应用统计学</td>\n",
       "      <td>本科</td>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1582598236647063552</td>\n",
       "      <td>黄先生</td>\n",
       "      <td>男</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师|数据挖掘工程师|机器学习工程师</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>2022-10-19</td>\n",
       "      <td>广东省</td>\n",
       "      <td>...</td>\n",
       "      <td>韩山师范学院</td>\n",
       "      <td>统计学</td>\n",
       "      <td>本科</td>\n",
       "      <td>2019-09-15</td>\n",
       "      <td>2023-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16136</th>\n",
       "      <td>7539911453372509716</td>\n",
       "      <td>陈先生</td>\n",
       "      <td>男</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师|数据挖掘工程师</td>\n",
       "      <td>4000</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16137</th>\n",
       "      <td>7539911474847346196</td>\n",
       "      <td>易女士</td>\n",
       "      <td>女</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师|数据挖掘工程师</td>\n",
       "      <td>4000</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16138</th>\n",
       "      <td>7539911492027215380</td>\n",
       "      <td>李女士</td>\n",
       "      <td>女</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师|数据挖掘工程师</td>\n",
       "      <td>4000</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16139</th>\n",
       "      <td>7539911483437280788</td>\n",
       "      <td>林女士</td>\n",
       "      <td>女</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师|数据挖掘工程师</td>\n",
       "      <td>4000</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16140</th>\n",
       "      <td>7539911500617149972</td>\n",
       "      <td>范先生</td>\n",
       "      <td>男</td>\n",
       "      <td>毕业找工作</td>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师|数据挖掘工程师</td>\n",
       "      <td>4000</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16141 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  resumeId username gender jobStatus  exp  \\\n",
       "0      1635610357617786880      欧先生      男     毕业找工作    0   \n",
       "1      1634019092572798976      赖女士      女     毕业找工作    0   \n",
       "2      1630818546172952576      陈先生      男     毕业找工作    0   \n",
       "3      1630884821167374336      李女士      女   无明确就业状态    1   \n",
       "4      1582598236647063552      黄先生      男     毕业找工作    1   \n",
       "...                    ...      ...    ...       ...  ...   \n",
       "16136  7539911453372509716      陈先生      男     毕业找工作    0   \n",
       "16137  7539911474847346196      易女士      女     毕业找工作    0   \n",
       "16138  7539911492027215380      李女士      女     毕业找工作    0   \n",
       "16139  7539911483437280788      林女士      女     毕业找工作    0   \n",
       "16140  7539911500617149972      范先生      男     毕业找工作    0   \n",
       "\n",
       "              expectPosition  willSalaryStart  willSalaryEnd  updateTime  \\\n",
       "0        数据分析师|数据挖掘工程师|算法工程师             3000           4000  2023-03-14   \n",
       "1                      数据分析师             4000           5000  2023-03-10   \n",
       "2        数据分析师|数据挖掘工程师|算法工程师             6000           8000  2023-03-01   \n",
       "3                      数据分析师             5000           8000  2023-03-01   \n",
       "4      数据分析师|数据挖掘工程师|机器学习工程师             8000          12000  2022-10-19   \n",
       "...                      ...              ...            ...         ...   \n",
       "16136          数据分析师|数据挖掘工程师             4000           6000         NaN   \n",
       "16137          数据分析师|数据挖掘工程师             4000           6000         NaN   \n",
       "16138          数据分析师|数据挖掘工程师             4000           6000         NaN   \n",
       "16139          数据分析师|数据挖掘工程师             4000           6000         NaN   \n",
       "16140          数据分析师|数据挖掘工程师             4000           6000         NaN   \n",
       "\n",
       "      province  ...     学校1    专业1  学位1       起始时间1       毕业时间1  学校2  专业2  \\\n",
       "0          广东省  ...     NaN    NaN  NaN         NaN         NaN  NaN  NaN   \n",
       "1          江苏省  ...     NaN    NaN  NaN         NaN         NaN  NaN  NaN   \n",
       "2          广东省  ...  韩山师范学院    统计学   本科  2019-09-15  2023-06-30  NaN  NaN   \n",
       "3          广东省  ...    韶关学院  应用统计学   本科  2019-03-09  2023-03-06  NaN  NaN   \n",
       "4          广东省  ...  韩山师范学院    统计学   本科  2019-09-15  2023-06-15  NaN  NaN   \n",
       "...        ...  ...     ...    ...  ...         ...         ...  ...  ...   \n",
       "16136      NaN  ...     NaN    NaN  NaN         NaN         NaN  NaN  NaN   \n",
       "16137      NaN  ...     NaN    NaN  NaN         NaN         NaN  NaN  NaN   \n",
       "16138      NaN  ...     NaN    NaN  NaN         NaN         NaN  NaN  NaN   \n",
       "16139      NaN  ...     NaN    NaN  NaN         NaN         NaN  NaN  NaN   \n",
       "16140      NaN  ...     NaN    NaN  NaN         NaN         NaN  NaN  NaN   \n",
       "\n",
       "       学位2 起始时间2 毕业时间2  \n",
       "0      NaN   NaN   NaN  \n",
       "1      NaN   NaN   NaN  \n",
       "2      NaN   NaN   NaN  \n",
       "3      NaN   NaN   NaN  \n",
       "4      NaN   NaN   NaN  \n",
       "...    ...   ...   ...  \n",
       "16136  NaN   NaN   NaN  \n",
       "16137  NaN   NaN   NaN  \n",
       "16138  NaN   NaN   NaN  \n",
       "16139  NaN   NaN   NaN  \n",
       "16140  NaN   NaN   NaN  \n",
       "\n",
       "[16141 rows x 37 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "People = pd.read_csv(\"../ProcessData/People.csv\")\n",
    "PeopleDetail = pd.read_csv(\"../ProcessData/PeopleDetail.csv\")\n",
    "data = People.merge(PeopleDetail)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resumeId                                                   1630884821167374336\n",
       "username                                                                   李女士\n",
       "gender                                                                       女\n",
       "jobStatus                                                              无明确就业状态\n",
       "exp                                                                          1\n",
       "expectPosition                                                           数据分析师\n",
       "willSalaryStart                                                           5000\n",
       "willSalaryEnd                                                             8000\n",
       "updateTime                                                          2023-03-01\n",
       "province                                                                   广东省\n",
       "city                                                                       广州市\n",
       "region                                                                     白云区\n",
       "birthday                                                            2000-09-30\n",
       "arrivalTime                                                               时间待议\n",
       "politicalStatus                                                             团员\n",
       "selfEvaluation               为人正直真诚，活泼开朗，且好学上进，工作学习态度严谨认真，责任心强；有较强的组织能力和适应能...\n",
       "expectIndustry                                                      电子商务|游戏|不限\n",
       "willNature                                                                  全职\n",
       "keywordList                                                             大数据分析师\n",
       "projectExperienceList        电商营销用户行为数据分析|广东泰迪智能科技股份有限公司|数据分析师|2022-09-07|2...\n",
       "competitionExperienceList      第九届泰迪杯数据挖掘挑战赛:三等奖| 全国大学生统计建模大赛:一等奖| 美国数学建模竞赛:S奖\n",
       "trainingExperienceList          广东泰迪智能科技股份有限公司|数据分析|2022-03-06|2022-03-11|None\n",
       "skillList                                   Python良好| MySQL良好| SPSS良好| Excel良好\n",
       "languageList                                                            R|GOOD\n",
       "certList                     CBDA技能证书|大数据分析工程师|2023-02-22|/userbq7ikv/16776...\n",
       "workExperienceList           None|广东泰迪智能科技股份有限公司|智能硬件|互联网|数据服务|数据分析师|2022-0...\n",
       "age                                                                       22.0\n",
       "学校1                                                                       韶关学院\n",
       "专业1                                                                      应用统计学\n",
       "学位1                                                                         本科\n",
       "起始时间1                                                               2019-03-09\n",
       "毕业时间1                                                               2023-03-06\n",
       "学校2                                                                        NaN\n",
       "专业2                                                                        NaN\n",
       "学位2                                                                        NaN\n",
       "起始时间2                                                                      NaN\n",
       "毕业时间2                                                                      NaN\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = data.iloc[3]\n",
    "test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.分词，提取关键词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['能力', '工作', '沟通', '团队', '乐于', '适应能力', '协作', '好学上进', '学习态度', '压力']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextRank4GetKeyWord(test['selfEvaluation'],num=10,word_min_len=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['工作压力', '乐于沟通', '沟通能力', '团队协作能力']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextRank4GetKeyWords(test['selfEvaluation'],num=10,min_occur_num=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['有较强的组织能力和适应能力，并具有良好的身体素质，乐于沟通，具备良好的沟通能力和团队协作能力，有团队精神，能快速融入团队',\n",
       " '有很强的抗压能力，能承担较大的工作量及较强的工作压力',\n",
       " '为人正直真诚，活泼开朗，且好学上进，工作学习态度严谨认真，责任心强']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextRank4GetSentence(test['selfEvaluation'],num=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  ['精神', 0.18602329091931022],\n",
       "  ['组织', 0.21304064488357943],\n",
       "  ['自学能力', 0.24707848541614305],\n",
       "  ['诚恳', 0.25922711654793656],\n",
       "  ['踏实', 0.22006113145187386],\n",
       "  ['面对', 0.22790936771605508]],\n",
       " [['严谨', 0.15105170074824198],\n",
       "  ['优异', 0.21227380115048158],\n",
       "  ['充满热情', 0.21227380115048158],\n",
       "  ['压力', 0.17180819387561386],\n",
       "  ['吃苦', 0.1569487689001353],\n",
       "  ['团结', 0.1943674846751337],\n",
       "  ['意识', 0.3138975378002706],\n",
       "  ['成绩', 0.17180819387561386],\n",
       "  ['扎实', 0.17180819387561386],\n",
       "  ['承受', 0.18166275094936177],\n",
       "  ['机会', 0.1637564344740139],\n",
       "  ['水平', 0.1943674846751337],\n",
       "  ['灵活', 0.17180819387561386],\n",
       "  ['珍惜', 0.18166275094936177],\n",
       "  ['班干部', 0.1943674846751337],\n",
       "  ['生活', 0.17180819387561386],\n",
       "  ['能力', 0.21855915949895044],\n",
       "  ['自学能力', 0.1637564344740139],\n",
       "  ['自我管理', 0.18166275094936177],\n",
       "  ['较大', 0.18166275094936177],\n",
       "  ['较强', 0.29170023599733197],\n",
       "  ['领导', 0.1943674846751337]],\n",
       " [['乐观', 0.4842072859873443],\n",
       "  ['吃苦耐劳', 0.5932341904474243],\n",
       "  ['开朗', 0.6431302352408564]],\n",
       " [['主动', 0.20664316454185422],\n",
       "  ['亲和力', 0.23377646438905317],\n",
       "  ['刻苦', 0.20664316454185422],\n",
       "  ['务实', 0.2553133760943978],\n",
       "  ['勤奋', 0.23377646438905317],\n",
       "  ['善于', 0.15588968362947828],\n",
       "  ['工作', 0.2160860022597877],\n",
       "  ['应变能力', 0.2553133760943978],\n",
       "  ['开朗', 0.19695887064792716],\n",
       "  ['很强', 0.23377646438905317],\n",
       "  ['情况', 0.2184957823532718],\n",
       "  ['意识', 0.18877091682805652],\n",
       "  ['服务', 0.23377646438905317],\n",
       "  ['沟通', 0.25589085636840775],\n",
       "  ['突发', 0.2553133760943978],\n",
       "  ['耐心', 0.2184957823532718],\n",
       "  ['能力', 0.2628731222864071],\n",
       "  ['责任心', 0.16982557080072827],\n",
       "  ['较强', 0.17542195894258253]],\n",
       " [],\n",
       " [['具备', 0.23523637997941713],\n",
       "  ['内容', 0.38526599678987744],\n",
       "  ['善于', 0.23523637997941713],\n",
       "  ['工作', 0.32607217971288865],\n",
       "  ['思考', 0.38526599678987744],\n",
       "  ['操守', 0.3297085200565191],\n",
       "  ['数据分析', 0.2185935665898026],\n",
       "  ['熟练', 0.25626553068164193],\n",
       "  ['环境', 0.2848539177587416],\n",
       "  ['职业', 0.35276695626617477],\n",
       "  ['软件', 0.24165200279945817]],\n",
       " [['耐心', 1.0]],\n",
       " [['严谨', 0.16712344099818022],\n",
       "  ['主修', 0.2348595077466868],\n",
       "  ['事物', 0.19008840291067852],\n",
       "  ['努力', 0.2009914743724335],\n",
       "  ['基础', 0.16136841537821195],\n",
       "  ['大学', 0.1736479509273189],\n",
       "  ['思维', 0.15622036953642526],\n",
       "  ['性格', 0.19008840291067852],\n",
       "  ['探索', 0.2009914743724335],\n",
       "  ['擅长', 0.21504797824957603],\n",
       "  ['数学', 0.19008840291067852],\n",
       "  ['活跃', 0.2348595077466868],\n",
       "  ['熟练', 0.15622036953642526],\n",
       "  ['理论', 0.2348595077466868],\n",
       "  ['紧密', 0.2348595077466868],\n",
       "  ['细心', 0.15622036953642526],\n",
       "  ['统计学', 0.19008840291067852],\n",
       "  ['计算机', 0.2009914743724335],\n",
       "  ['诚恳', 0.19008840291067852],\n",
       "  ['语言', 0.33424688199636043],\n",
       "  ['责任心', 0.15622036953642526],\n",
       "  ['踏实', 0.16136841537821195],\n",
       "  ['逻辑', 0.2009914743724335]],\n",
       " [['事物', 0.17253747917811924],\n",
       "  ['亲和力', 0.1951925288517877],\n",
       "  ['基础知识', 0.1951925288517877],\n",
       "  ['展现', 0.21317485342158612],\n",
       "  ['工具', 0.18243386652476895],\n",
       "  ['开放', 0.21317485342158612],\n",
       "  ['心态', 0.16445154195497047],\n",
       "  ['懂得', 0.21317485342158612],\n",
       "  ['扎实', 0.17253747917811924],\n",
       "  ['技巧', 0.21317485342158612],\n",
       "  ['技能', 0.17253747917811924],\n",
       "  ['接受', 0.17253747917811924],\n",
       "  ['收集', 0.1951925288517877],\n",
       "  ['整理', 0.1951925288517877],\n",
       "  ['有条理', 0.1951925288517877],\n",
       "  ['熟练地', 0.1951925288517877],\n",
       "  ['理解', 0.1951925288517877],\n",
       "  ['生活', 0.17253747917811924],\n",
       "  ['知识', 0.16445154195497047],\n",
       "  ['统计学', 0.17253747917811924],\n",
       "  ['责任感', 0.15169287962795178],\n",
       "  ['身体健康', 0.21317485342158612],\n",
       "  ['配合', 0.1951925288517877],\n",
       "  ['预处理', 0.21317485342158612]],\n",
       " [['三个', 0.21142345724826156],\n",
       "  ['以上学历', 0.21142345724826156],\n",
       "  ['仪表', 0.21142345724826156],\n",
       "  ['保证', 0.19358887137246725],\n",
       "  ['全日制', 0.18093503131701394],\n",
       "  ['公司', 0.15044660538576635],\n",
       "  ['办公', 0.1631004454412196],\n",
       "  ['协调', 0.1631004454412196],\n",
       "  ['品行', 0.19358887137246725],\n",
       "  ['大学本科', 0.21142345724826156],\n",
       "  ['大方', 0.21142345724826156],\n",
       "  ['实习', 0.18093503131701394],\n",
       "  ['应变', 0.17111995044085296],\n",
       "  ['形象', 0.21142345724826156],\n",
       "  ['得体', 0.21142345724826156],\n",
       "  ['意识', 0.1563200505756325],\n",
       "  ['文档', 0.21142345724826156],\n",
       "  ['服务', 0.19358887137246725],\n",
       "  ['每周', 0.21142345724826156],\n",
       "  ['气质佳', 0.21142345724826156],\n",
       "  ['端庄', 0.21142345724826156],\n",
       "  ['管理', 0.18093503131701394],\n",
       "  ['言谈举止', 0.21142345724826156],\n",
       "  ['设备', 0.19358887137246725]],\n",
       " [['作风', 0.22649186269462168],\n",
       "  ['办公', 0.17472481140504145],\n",
       "  ['勇于', 0.18331587622299567],\n",
       "  ['守信', 0.20738618431829733],\n",
       "  ['待人', 0.17472481140504145],\n",
       "  ['性格开朗', 0.15561913302871708],\n",
       "  ['执行力', 0.22649186269462168],\n",
       "  ['挑战', 0.17472481140504145],\n",
       "  ['操作', 0.18331587622299567],\n",
       "  ['文书', 0.22649186269462168],\n",
       "  ['时间', 0.15561913302871708],\n",
       "  ['正派', 0.22649186269462168],\n",
       "  ['热心', 0.22649186269462168],\n",
       "  ['热爱', 0.15561913302871708],\n",
       "  ['熟练地', 0.20738618431829733],\n",
       "  ['生活习惯', 0.22649186269462168],\n",
       "  ['网络软件', 0.22649186269462168],\n",
       "  ['自我', 0.17472481140504145],\n",
       "  ['表达', 0.22649186269462168],\n",
       "  ['观念', 0.22649186269462168],\n",
       "  ['诚实', 0.1938304897813658],\n",
       "  ['责任心', 0.1506545033097398],\n",
       "  ['较强', 0.15561913302871708],\n",
       "  ['道德修养', 0.22649186269462168]],\n",
       " [['不断完善', 0.18317136071953413],\n",
       "  ['价值', 0.1619115496995875],\n",
       "  ['任职', 0.17119845605382528],\n",
       "  ['充分发挥', 0.18317136071953413],\n",
       "  ['创造', 0.18317136071953413],\n",
       "  ['各类', 0.20004622206656528],\n",
       "  ['四年', 0.17119845605382528],\n",
       "  ['培养', 0.18317136071953413],\n",
       "  ['实操', 0.18317136071953413],\n",
       "  ['搭建', 0.18317136071953413],\n",
       "  ['数据分析', 0.22700584805669058],\n",
       "  ['条理性', 0.17119845605382528],\n",
       "  ['沟通交流', 0.18317136071953413],\n",
       "  ['熟练掌握', 0.18317136071953413],\n",
       "  ['积极向上', 0.1543235947067941],\n",
       "  ['系统', 0.17119845605382528],\n",
       "  ['经历', 0.3086471894135882],\n",
       "  ['自我', 0.1543235947067941],\n",
       "  ['谨慎', 0.18317136071953413],\n",
       "  ['足够', 0.18317136071953413],\n",
       "  ['项目', 0.2748974667195259]]]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords = pd.read_csv(\"../../mix_stopwords.txt\",index_col=False,sep=\"\\t\",quoting=3,names=['stopword'], encoding='utf-8').stopword.values.tolist() #list\n",
    "cut_words = cut_word(data['selfEvaluation'].dropna())\n",
    "TFIDFGetKeyWord(Dropwords(stopwords,cut_words),threshold=0.15)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里关键词的选择可以使用TextRank4GetKeyWords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'电商营销用户行为数据分析|广东泰迪智能科技股份有限公司|数据分析师|2022-09-07|2022-09-08|项目简介：跟随网络电商发展策略，用专业的数据进行理性分析，在合适的时机做正确的事，让店铺运营少走弯路，减少运营的试错成本。提供分析与可视化结果，比如注册量，留存率、注册-活跃-购买的转化率、游戏用户的价值分析等。\\\\n项目岗位：数据分析\\\\n项目职责：数据存储、数据清洗、统计以日、月为单位的人数点击量、访问量等、客户价值分群结果、用户流失的预警|进行数据统计分析后得出一些建议与营销策略| 京东花西子散粉评论数据采集与分析|广东泰迪智能科技股份有限公司|数据分析师|2022-09-15|2022-09-16|项目简介：获取了自己喜爱的花西子品牌下的散粉评论数据，进行自然语言识别，给不同类型商品进行评分分析与可视化，挑选出受欢迎的款式。\\\\n项目职责：\\\\n1.\\\\xa0\\\\xa0 评论数据获取；2.\\\\xa0\\\\xa0 评论数据预处理；3.\\\\xa0\\\\xa0 评分数据分析；4.\\\\xa0\\\\xa0 撰写分析报告|分析出几件散粉中销量最高最受欢迎的，以及客户对该商品的喜爱程度'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project = test['projectExperienceList']\n",
    "TextRank4GetKeyWords(project,num=20,min_occur_num=1)\n",
    "project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'None|广东泰迪智能科技股份有限公司|智能硬件|互联网|数据服务|数据分析师|2022-02-07|2022-03-12|岗位职能：数据储存、数据可视化、数据采集、数据挖掘、人工智能算法、Linux等\\\\n学习工具：MySQL、Numpy、Pandas、Matplotlib等可视化、Selenium|scrapy框架、朴素贝叶斯等分类回归、K-Means等聚类、深度学习TensorFlow、Linux Vi、Hadoop等等'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workExperience = test['workExperienceList']\n",
    "TextRank4GetKeyWords(workExperience,num=20,min_occur_num=1)\n",
    "workExperience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "wp = project+workExperience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def data_filter(x):\n",
    "    new_text = re.sub(r'[\\d\\n【】()岗位职责：、岗位要求：（）Ø . |None\\\\-xa0]', '',x)\n",
    "    return new_text\n",
    "aa = data_filter(wp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['花西子', '营销策略', '项目数据', '进行数据', '挑选出受欢迎']\n",
      "['数据可视化数据', '喜爱程度']\n"
     ]
    }
   ],
   "source": [
    "for i in TextRank4GetSentence(aa,num=2):\n",
    "    print(TextRank4GetKeyWords(i,num=10))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.编制词云图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'技能：Python良好, MySQL良好, SPSS良好, Excel良好'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Listsplit(x,extra_info):\n",
    "    if isinstance(x,str):\n",
    "        x = x.split(\"|\")\n",
    "        x = \",\".join(x)\n",
    "        return f\"{extra_info}：{x}\"\n",
    "skill = Listsplit(test['skillList'],\"技能\")\n",
    "skill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'竞赛：第九届泰迪杯数据挖掘挑战赛:三等奖, 全国大学生统计建模大赛:一等奖, 美国数学建模竞赛:S奖'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "competition = Listsplit(test['competitionExperienceList'],\"竞赛\")\n",
    "competition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'培训经历：广东泰迪智能科技股份有限公司————数据分析'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainExperience = Listsplit(test['trainingExperienceList'],\"培训经历\")\n",
    "_ = trainExperience.split(\",\")\n",
    "trainExperience =  _[0]+\"————\"+_[1]\n",
    "trainExperience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'编程语言：R,GOOD'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# language去掉\n",
    "language = Listsplit(test[\"languageList\"],\"编程语言\")\n",
    "language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'证书：CBDA技能证书,泰迪科技实习证明'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cert = Listsplit(test['certList'],\"证书\")\n",
    "cert = \",\".join([i.split(\",\")[0] for i in cert.split(\" \")])\n",
    "cert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'大数据分析师'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumekeyword = test['keywordList']\n",
    "resumekeyword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "school = test['学校1']\n",
    "degree = test['学位1']\n",
    "major = test['专业1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {'求职者resumeId':5,\"性别gender\":1,\"jobStatus\":1,\n",
    "       \"经验exp年以上\":1,\"期望职业expectPosition\":1,\n",
    "       \"期望底薪willSalaryStart\":1,\"期望顶薪willSalaryEnd\":1,\n",
    "       \"到岗arrivalTime\":1,\"政治面貌politicalStatus\":1,\n",
    "       \"年龄age\":1}\n",
    "\n",
    "all_list = [skill,competition,trainExperience,cert,school,degree,major]\n",
    "def PeopleProdileKeyWord(test,dic,all_list):\n",
    "    profile = []\n",
    "\n",
    "    for k,v in dic.items():\n",
    "        res = []\n",
    "        try:\n",
    "            print(k,str(test[extra_english(k)].values[0]))\n",
    "        except:\n",
    "            print(k)\n",
    "        res.append(change_english(k,str(test[extra_english(k)].values[0])))\n",
    "        res = res*v\n",
    "        profile.append(res)\n",
    "\n",
    "    for i in range(len(all_list)):\n",
    "        profile.append([all_list[i]])\n",
    "    from collections import Counter\n",
    "    return Counter([word for line in profile for word in line])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "求职者resumeId 1630884821167374336\n",
      "性别gender 女\n",
      "jobStatus 无明确就业状态\n",
      "经验exp年以上 1\n",
      "期望职业expectPosition 数据分析师\n",
      "期望底薪willSalaryStart 5000\n",
      "期望顶薪willSalaryEnd 8000\n",
      "到岗arrivalTime 时间待议\n",
      "政治面貌politicalStatus 团员\n",
      "年龄age 22.0\n"
     ]
    }
   ],
   "source": [
    "profile = PeopleProdileKeyWord(test,dic,all_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 791.5, 811.5, -0.5)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#导入相关库\n",
    "import pandas\n",
    "import jieba\n",
    "from wordcloud import WordCloud,ImageColorGenerator \n",
    "import matplotlib.colors as colors\n",
    "from PIL import Image\n",
    "#%%\n",
    "text = \" \".join(profile)\n",
    "#%%\n",
    "#词云图\n",
    "colormaps = colors.ListedColormap(['#FF4500', '#FF7F50', '#FFD700'])\n",
    "mask1 = np.array(Image.open('求职者.jpg'))\n",
    "image_colors = ImageColorGenerator(mask1)\n",
    "\n",
    "wc = WordCloud(font_path='simhei.ttf',#字体文件路径（这里为黑体）\n",
    "            #    background_color='white',#背景颜色（这里为白色）\n",
    "                width=1000,#宽度\n",
    "                height=500,#高度\n",
    "                colormap =colormaps,\n",
    "                mask=mask1,\n",
    "                color_func=image_colors,\n",
    "                ).generate(text)# #绘制词云图\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.imshow(wc)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
